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Artificial Intelligence (AI) Analyst

Job Description - Artificial Intelligence (AI) Analyst

Factory Motor Parts is moving its AI and data platform past reporting and into production systems: retrieval over unstructured company documents, agents that query live business data, and automations that push AI output into daily operations. The AI Analyst builds and supports those systems end to end - retrieval pipelines, the agent and tool layer, the data models underneath them, and the evaluation and monitoring that keep the output trustworthy enough for people to act on.


 


This is a hands on, build focused role on a small team with wide scope. The right person writes Python and SQL comfortably, thinks clearly about how data is modeled, and iterates fast on systems where correctness matters more than novelty.


 


KEY RESPONSIBILITES



  • Design, build, and maintain RAG pipelines over FMP's internal documents - chunking, embedding, indexing, and hybrid retrieval - and continuously improve retrieval quality against measured baselines

  • Develop agent workflows and MCP tools that give AI governed, secure access to live FMP business data, including text-to-SQL against the analytics warehouse

  • Build and maintain ELT pipelines and dbt models that land and curate data in FMP's data lake for both analytics and AI consumption

  • Build and maintain production automations (n8n) that connect AI output to daily processes across finance, operations, sales, and warehouse teams

  • Build evaluation frameworks, monitoring, and cost controls so AI output is measured and trustworthy before it reaches users

  • Partner directly with Merchandising, Pricing, Finance, Operations, HR, and Legal to turn business problems into working systems, including teams new to AI tooling


 


REQUIRED QUALIFICATIONS



  • Bachelor's degree in Computer Science, Software Engineering, Data Science, or a related technical field

  • 2+ years of hands-on experience in software, data, or AI engineering

  • Strong Python and strong SQL

  • Working experience with LLM APIs, prompt design, and structured outputs

  • Demonstrated experience building a RAG pipeline end to end, including embeddings, vector search, and retrieval evaluation

  • Solid understanding of relational data modeling, joins, and query performance

  • Experience integrating systems through REST APIs and webhooks

  • Version control and basic software engineering discipline, including code review and testing

  • Clear written communication and the ability to explain technical tradeoffs to non-technical stakeholders


 


PREFERRED QUALIFICATIONS



  • Vector databases such as Pinecone, Weaviate, or Qdrant

  • Agent frameworks and orchestration: LangGraph, LangChain, LlamaIndex, or the Claude Agent SDK

  • Model Context Protocol (MCP) and tool design for agent access to internal systems

  • dbt for transformation, testing, documentation, and lineage

  • Cloud data platform experience — Snowflake, AWS, or GCP preferred

  • Workflow automation experience, n8n preferred (or Make, Zapier, Airflow)

  • LLM evaluation and observability tooling such as RAGAS, Langfuse, or LangSmith


 


TECHNOLOGY ENVIRONMENT


Claude Enterprise and the Anthropic API, MCP, Python, Firebird SQL, Snowflake, Apache Doris, dbt, n8n, Pinecone, Power BI, Google Workspace, on-premises Linux infrastructure, and a large purpose-built analytics warehouse spanning sales, product, customer, inventory, fleet, HR, and finance data


 


WHY THIS ROLE


FMP already runs Claude Enterprise across the organization, a purpose-built analytics database wired directly to AI through MCP, and a growing portfolio of production automations. This is not a research seat or an indefinite pilot — work reaches real users quickly, ownership is real, and the scope will grow with the person who fills it.


We are an EEOC/AA Employer.


An industry leader, FMP offers well-balanced compensation and benefits programs, which may include medical, dental, vision, life, 401K, profit sharing, paid holidays/vacation/sick time, STD/LTD.

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